What You Should Know About AI-powered Review Responses

The key to responding to consumer reviews is empathy — but how do you scale empathy?

As a best practice, each review on each platform deserves a personalized, sensitive reply. But the digital landscape is growing so quickly: your consumers are leaving reviews on your listings with third-party publishers, on social media, and more. And with every new app or platform, your team is forced to re-prioritize the channels in their review response strategy because they just can't manage all of them.

Some look to the launch of large language models (LLMs) for breathing room. Surely, with the right prompt, AI could alleviate some of the workload?

But the answer isn't so simple. AI can be used to enhance your review response strategy, but as with all tools, it still has to be leveraged carefully.

The sheer amount of reviews that require responses can overwhelm brands (and teams). And the quantity of reviews increases exponentially with each location — or, in the case of agencies, with each client's brand.

Even if you do have enough bandwidth to respond to each review manually, is that really the best use of your team's time? This takes a lot of resources away from business priorities. Not to mention: manually managing consumer feedback on every individual publisher (like your Google Business Profile, Facebook business profiles, and industry-specific platforms like TripAdvisor) can lead to inconsistencies. AI hasn't mastered empathy yet, but humans also make errors.

Still, LLMs like ChatGPT aren't ready to take over replying to reviews entirely. For one, LLM technology does not have access to real-time information about your products, services, or specific customer interactions. It may not have the up-to-date knowledge it needs to understand the context of a review, leading to generic or inaccurate responses. And, in certain industries like healthcare and financial services, there are legal and compliance nuances to consider.

LLMs may also struggle to understand the implied tone and emotions within consumer reviews — and fail to empathize as a result. To be effective, your reviews should still be on-brand, personalized, respond with the correct tone, and include accurate information about your business. This is especially true in the age of AI because consumers are getting better at picking up on AI-generated responses, and they may take offense if they feel their experiences aren't seriously considered by the company.

It's clear that humans have limited bandwidth, and LLMs' infinite scalability isn't exactly helpful when errors come up in consumer-facing communications. But if you combine the empathy and knowledge of your team with the capabilities of LLMs, you can augment your review response strategy.

The key is to rely on your team, not replace them. They have the necessary skills for support, and LLMs should magnify that — not imitate it.

Humans should be in-the-loop for all review responses, regardless of whether it is a simple situation or a complicated one.

For no context reviews (i.e., star-ratings only), you could use a template. But for complex, context-heavy reviews, AI content generation shines. This is because the LLMs can understand consumer sentiment and the complexity of their experience, then generate a first draft of a review response with just a click of a button.

This gives the human reviewer something to work off of and saves their (very finite) time. Instead of writing a review response from scratch, your team simply reviews the response, makes any necessary edits, and sends it to the publish queue.

Following review response best practices is extremely important. If you're augmenting your engagement strategy with AI, you need to make sure that your solution will make it easier (not harder) to flow between AI-powered and manual workflows.

When evaluating tools, look for these four capabilities:

1. Your team should have the ability to give the LLM instructions on the tone or language for outputs.

Your team are experts in customer service. Make sure they can customize the instructions given to the LLM. Then, the solution will have guidelines on how to generate review responses, like which language to respond in, the tone to use, or how many sentences the response should be.

It's also helpful if your team can embed fields within the instructions that are contextualized for specific situations. As an example, the AI could include the customer service entity that a reviewer should reach out to for additional assistance.

2. The LLM should be trained on your brand's tone, response style, and best practices.

For faster time-to-value, your review response tool should learn from previous replies to review. This will help your tool to generate relevant, on-brand responses — and it'll help your team reply to consumers faster, too.

3. The review response tool should be able to pull important business information into its AI-generated response.

Think of all the information about your business that your consumers may need: your location, hours of operation, your products and services, and even your customer service contact information. This may be important to include in the reply, and it requires that the LLM have lots of context about your business. To account for this, look for a review response tool that can also learn from your structured data, like the content in your CMS.

4. Administrative controls are necessary to guard your brand.

For any generative AI tool (and especially any tools that integrate with other platforms, like your CMS), your team leader needs the ability to restrict or grant user permissions. You may have some users that simply review and approve or deny the AI-generated reply, or you may have other users that have the permissions to edit review responses. However your team is structured, make sure the review response tool can accommodate it.

AI-powered content generation is a very powerful tool, and when combined with your review response strategy, your team can scale empathetic, brand-approved engagement with consumers.

And in addition to engaging customers, this hybrid human-machine approach can save your organization valuable time, resources, and money.

As long as you have the appropriate guardrails in place, you can keep an empathetic, human voice at the center of your AI-generated content. Keep consumers satisfied and improve your brand reputation — all without overwhelming your team.